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Netlab Reference Manual gpcovar
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<H1> gpcovar
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<h2>
Purpose
</h2>
Calculate the covariance for a Gaussian Process.

<p><h2>
Synopsis
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<PRE>
cov = gpcovar(net, x)
[cov, covf] = gpcovar(net, x)
</PRE>


<p><h2>
Description
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<p><CODE>cov = gpcovar(net, x)</CODE> takes 
a Gaussian Process data structure <CODE>net</CODE> together with
a matrix <CODE>x</CODE> of input vectors, and computes the covariance
matrix <CODE>cov</CODE>.  The inverse of this matrix is used when calculating
the mean and variance of the predictions made by <CODE>net</CODE>.

<p><CODE>[cov, covf] = gpcovar(net, x)</CODE> also generates the covariance
matrix due to the covariance function specified by <CODE>net.covarfn</CODE>
as calculated by <CODE>gpcovarf</CODE>.

<p><h2>
Example
</h2>
In the following example, the inverse covariance matrix is calculated
for a set of training inputs <CODE>x</CODE> and is then
passed to <CODE>gpfwd</CODE> so that predictions (with mean <CODE>ytest</CODE> and
variance <CODE>sigsq</CODE>) can be made for the test inputs
<CODE>xtest</CODE>.
<PRE>

cninv = inv(gpcovar(net, x)); 
[ytest, sigsq] = gpfwd(net, xtest, cninv);
</PRE>


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See Also
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<CODE><a href="gp.htm">gp</a></CODE>, <CODE><a href="gppak.htm">gppak</a></CODE>, <CODE><a href="gpunpak.htm">gpunpak</a></CODE>, <CODE><a href="gpcovarp.htm">gpcovarp</a></CODE>, <CODE><a href="gpcovarf.htm">gpcovarf</a></CODE>, <CODE><a href="gpfwd.htm">gpfwd</a></CODE>, <CODE><a href="gperr.htm">gperr</a></CODE>, <CODE><a href="gpgrad.htm">gpgrad</a></CODE><hr>
<b>Pages:</b>
<a href="index.htm">Index</a>
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<p>Copyright (c) Ian T Nabney (1996-9)


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